We demonstrate our hypothesis in the CARLA simulator [1]}. All trials are started with random initialization of the player and non-player positions and goals and the non-player vehicles are induced with temporal noise to increase stochasticity of the multi-player dynamics. Such a stochastic setting makes it more difficult for the agent to overfit to a deterministic non-player policy and requires that the player have temporal information about the non-player agents to infer their present state without which it cannot derive it's own policy. The demonstrated expert policy is also extremely opportunistic as the demonstrator tries to not only avoid all collisions but also reach the goal locations in the least amount of time, generously overtaking vehicles if required. These changes make the driving conditions more realistic and challenging for a machine learning algorithm.
